Clinical outcomes of dupilumab therapy in chronic rhinosinusitis with nasal polyps in a Canadian tertiary care rhinology practice
Bibliographic record
Abstract
BACKGROUND: In 2020, dupilumab became the first monoclonal antibody therapy to be approved by Health Canada for the treatment of chronic rhinosinusitis with nasal polyps (CRSwNP). The primary aim of this study was to characterize the outcomes in an initial cohort of patients with CRSwNP who have undergone dupilumab therapy. METHODS: A retrospective study was conducted of patients with CRSwNP who were treated with dupilumab. Demographic information, comorbidities, number of previous surgeries, and insurance information were collected. The primary outcome were changes in the sinonasal outcome test (SNOT-22) scores from baseline to timepoints after receiving dupilumab. RESULTS: Forty-eight patients were considered for dupilumab therapy, and 27 (56%) received coverage or were able to fund the medication independently. Patients waited an average of 3.6 months to obtain access to the medication. The mean age of the patients was 43. Forty-one percent (11/27) of patients had aspirin exacerbated respiratory disease, and 96% (26/27) had a diagnosis of asthma. The mean length of time on dupilumab was 12.1 months. The baseline SNOT-22 score was 60.6. The mean decrease at 1 month, 3 months, 6 months, and 12 months after starting dupilumab was 8.8, 26.5, 42.8, and 33.8, respectively. There were no serious adverse events. CONCLUSION: Patients treated with dupilumab in a Canadian tertiary care rhinology clinic demonstrated substantial clinical improvement as measured by disease-specific sinonasal outcomes. Further studies are needed to determine the longer-term effectiveness and adverse event profile of this novel therapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".